Calculating the nutrient composition of recipes with computers

Powers, Peter M.; Hoover, Loretta W.

doi:10.1016/s0002-8223(21)02100-3

Cited by 28 publications

(3 citation statements)

References 15 publications

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“…The percentage total sugar content of foods is used to estimate gram equivalents of added sugars in caloric sweeteners. The number of added sugars equivalents for each sweetener ingredient in a multi-ingredient food is calculated using the recipe retention factor method (14) and then totaled: total sugars in the sweetener ingredient in 100 g of a multi-ingredient food (grams) = [weight of sweetener ingredient in 100 grams of food × (total sugars in 100 g ingredient/100) × (% retention)]/100% + (%moisture change) + (%fat change).…”

Section: Ascertainment Of Dietary Variablesmentioning

confidence: 99%

High glycemic index and glycemic load diets as risk factors for insomnia: analyses from the Women’s Health Initiative

Gangwisch

Hale

St‐Onge

et al. 2020

The American Journal of Clinical Nutrition

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Background Previous studies have shown mixed results on the association between carbohydrate intake and insomnia. However, any influence that refined carbohydrates have on risk of insomnia is likely commensurate with their relative contribution to the overall diet, so studies are needed that measure overall dietary glycemic index (GI), glycemic load, and intakes of specific types of carbohydrates. Objective We hypothesized that higher GI and glycemic load would be associated with greater odds of insomnia prevalence and incidence. Methods This was a prospective cohort study with postmenopausal women who participated in the Women's Health Initiative Observational Study, investigating the relations of GI, glycemic load, other carbohydrate measures (added sugars, starch, total carbohydrate), dietary fiber, and specific carbohydrate-containing foods (whole grains, nonwhole/refined grains, nonjuice fruits, vegetables, dairy products) with odds of insomnia at baseline (between 1994 and 1998; n = 77,860) and after 3 y of follow-up (between 1997 and 2001; n = 53,069). Results In cross-sectional and longitudinal analyses, higher dietary GI was associated with increasing odds of prevalent (fifth compared with first quintile OR: 1.11; CI: 1.05, 1.16; P-trend = 0.0014) and incident (fifth compared with first quintile OR: 1.16; CI: 1.08, 1.25; P-trend < 0.0001) insomnia in fully adjusted models. Higher intakes of dietary added sugars, starch, and nonwhole/refined grains were each associated with higher odds of incident insomnia. By contrast, higher nonjuice fruit and vegetable intakes were significantly associated with lower odds of incident insomnia. Also, higher intakes of dietary fiber, whole grains, nonjuice fruit, and vegetables were significantly associated with lower odds of prevalent insomnia. Conclusions The results suggest that high-GI diets could be a risk factor for insomnia in postmenopausal women. Substitution of high-GI foods with minimally processed, whole, fiber-rich carbohydrates should be evaluated as potential treatments of, and primary preventive measures for, insomnia in postmenopausal women.

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Section: Ascertainment Of Dietary Variablesmentioning

confidence: 99%

High glycemic index and glycemic load diets as risk factors for insomnia: analyses from the Women’s Health Initiative

Gangwisch

Hale

St‐Onge

et al. 2020

The American Journal of Clinical Nutrition

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“…Approximately onethird of the FNDDS codes are a direct match to a single FDC code from either data set. The nutrient profiles for most foods and beverages in the FNDDS were generated using a recipe calculation process (62). In addition to selecting the appropriate ingredients and proportions for each recipe, nutrient retention and moisture change factors may be applied.…”

Section: Source Of Nutrient Valuesmentioning

confidence: 99%

National Food Intake Assessment: Technologies to Advance Traditional Methods

2022

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National dietary surveillance produces dietary intake data used for various purposes including development and evaluation of national policies in food and nutrition. Since 2000, What We Eat in America, the dietary component of the National Health and Nutrition Examination Survey, has collected dietary data and reported on the dietary intake of the US population. Continual innovations are required to improve methods of data collection, quality, and relevance. This review article evaluates the strengths and limitations of current and newer methods in national dietary data collection, underscoring the use of technology and emerging technology applications. We offer four objectives for national dietary surveillance that serve as guiding principles in the evaluation. Moving forward, national dietary surveillance must take advantage of new technologies for their potential in enhanced efficiency and objectivity in data operations while continuing to collect accurate dietary information that is standardized, validated, and publicly transparent.

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“…Computer vision can be applied to analyze pictures of meals to predict the nutritional content [12]. Other systems focus on analysing the written form of nutritional content ( [13], [14]). Recent attempts try to improve recipe recommendations by understanding the user's tastes [15].…”

Section: Previous Workmentioning

confidence: 99%

Food Recommendation Using Ontology and Heuristics

El-Dosuky

Rashad

Hamza

et al. 2012

Communications in Computer and Information Science

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Abstract. Recommender systems are needed to find food items of one's interest. We review recommender systems and recommendation methods. We propose a food personalization framework based on adaptive hypermedia. We extend Hermes framework with food recommendation functionality. We combine TF-IDF term extraction method with cosine similarity measure. Healthy heuristics and standard food database are incorporated into the knowledgebase. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the proposed recommender has a better F-measure than existing semantic recommenders.

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Calculating the nutrient composition of recipes with computers

Cited by 28 publications

References 15 publications

High glycemic index and glycemic load diets as risk factors for insomnia: analyses from the Women’s Health Initiative

High glycemic index and glycemic load diets as risk factors for insomnia: analyses from the Women’s Health Initiative

National Food Intake Assessment: Technologies to Advance Traditional Methods

Food Recommendation Using Ontology and Heuristics

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